Overview

Dataset statistics

Number of variables13
Number of observations3142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.2 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Text3
Numeric9

Alerts

ADSL is highly overall correlated with Total generalHigh correlation
CABLEMODEM is highly overall correlated with Total general and 1 other fieldsHigh correlation
DIAL UP is highly overall correlated with ProvinciaHigh correlation
FIBRA OPTICA is highly overall correlated with Total generalHigh correlation
Total general is highly overall correlated with ADSL and 2 other fieldsHigh correlation
Provincia is highly overall correlated with CABLEMODEM and 1 other fieldsHigh correlation
ADSL is highly skewed (γ1 = 30.21303493)Skewed
CABLEMODEM is highly skewed (γ1 = 46.14515313)Skewed
DIAL UP is highly skewed (γ1 = 36.89572645)Skewed
FIBRA OPTICA is highly skewed (γ1 = 20.54215104)Skewed
OTROS is highly skewed (γ1 = 26.77201648)Skewed
SATELITAL is highly skewed (γ1 = 54.61854948)Skewed
WIMAX is highly skewed (γ1 = 25.48340858)Skewed
Total general is highly skewed (γ1 = 39.3409081)Skewed
ADSL has 2016 (64.2%) zerosZeros
CABLEMODEM has 2295 (73.0%) zerosZeros
DIAL UP has 2760 (87.8%) zerosZeros
FIBRA OPTICA has 1743 (55.5%) zerosZeros
OTROS has 2537 (80.7%) zerosZeros
SATELITAL has 2089 (66.5%) zerosZeros
WIMAX has 3131 (99.6%) zerosZeros
WIRELESS has 934 (29.7%) zerosZeros

Reproduction

Analysis started2023-07-07 15:58:30.587637
Analysis finished2023-07-07 15:58:51.159745
Duration20.57 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Provincia
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
BUENOS AIRES
716 
CORDOBA
394 
SANTA FE
354 
ENTRE RIOS
150 
MENDOZA
145 
Other values (19)
1383 

Length

Max length19
Median length12
Mean length9.0461489
Min length4

Characters and Unicode

Total characters28423
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBUENOS AIRES
2nd rowBUENOS AIRES
3rd rowBUENOS AIRES
4th rowBUENOS AIRES
5th rowBUENOS AIRES

Common Values

ValueCountFrequency (%)
BUENOS AIRES 716
22.8%
CORDOBA 394
12.5%
SANTA FE 354
11.3%
ENTRE RIOS 150
 
4.8%
MENDOZA 145
 
4.6%
SANTIAGO DEL ESTERO 130
 
4.1%
SALTA 123
 
3.9%
RIO NEGRO 119
 
3.8%
SAN LUIS 97
 
3.1%
JUJUY 95
 
3.0%
Other values (14) 819
26.1%

Length

2023-07-07T10:58:51.354111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buenos 716
14.0%
aires 716
14.0%
cordoba 394
 
7.7%
santa 376
 
7.4%
fe 354
 
6.9%
san 177
 
3.5%
la 157
 
3.1%
entre 150
 
2.9%
rios 150
 
2.9%
mendoza 145
 
2.8%
Other values (23) 1776
34.7%

Most occurring characters

ValueCountFrequency (%)
A 3667
12.9%
E 3092
10.9%
S 2932
10.3%
O 2737
9.6%
N 2227
7.8%
R 2153
 
7.6%
1969
 
6.9%
I 1549
 
5.4%
U 1480
 
5.2%
T 1184
 
4.2%
Other values (13) 5433
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26454
93.1%
Space Separator 1969
 
6.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3667
13.9%
E 3092
11.7%
S 2932
11.1%
O 2737
10.3%
N 2227
8.4%
R 2153
8.1%
I 1549
 
5.9%
U 1480
 
5.6%
T 1184
 
4.5%
B 1174
 
4.4%
Other values (12) 4259
16.1%
Space Separator
ValueCountFrequency (%)
1969
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26454
93.1%
Common 1969
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3667
13.9%
E 3092
11.7%
S 2932
11.1%
O 2737
10.3%
N 2227
8.4%
R 2153
8.1%
I 1549
 
5.9%
U 1480
 
5.6%
T 1184
 
4.5%
B 1174
 
4.4%
Other values (12) 4259
16.1%
Common
ValueCountFrequency (%)
1969
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3667
12.9%
E 3092
10.9%
S 2932
10.3%
O 2737
9.6%
N 2227
7.8%
R 2153
 
7.6%
1969
 
6.9%
I 1549
 
5.4%
U 1480
 
5.2%
T 1184
 
4.2%
Other values (13) 5433
19.1%
Distinct432
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:52.219102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length31
Median length26
Mean length10.110757
Min length4

Characters and Unicode

Total characters31768
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)1.3%

Sample

1st row25 de Mayo
2nd row25 de Mayo
3rd row25 de Mayo
4th row25 de Mayo
5th row25 de Mayo
ValueCountFrequency (%)
san 410
 
8.0%
general 275
 
5.4%
de 181
 
3.5%
la 125
 
2.4%
martín 122
 
2.4%
río 83
 
1.6%
capital 69
 
1.3%
santa 62
 
1.2%
justo 58
 
1.1%
roca 57
 
1.1%
Other values (485) 3698
71.9%
2023-07-07T10:58:53.404909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4195
 
13.2%
e 2638
 
8.3%
n 2373
 
7.5%
r 2090
 
6.6%
o 2084
 
6.6%
1998
 
6.3%
l 1814
 
5.7%
i 1453
 
4.6%
s 1020
 
3.2%
t 997
 
3.1%
Other values (55) 11106
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24552
77.3%
Uppercase Letter 5049
 
15.9%
Space Separator 1998
 
6.3%
Decimal Number 94
 
0.3%
Other Punctuation 73
 
0.2%
Other Letter 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4195
17.1%
e 2638
10.7%
n 2373
9.7%
r 2090
8.5%
o 2084
8.5%
l 1814
 
7.4%
i 1453
 
5.9%
s 1020
 
4.2%
t 997
 
4.1%
u 986
 
4.0%
Other values (23) 4902
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 715
14.2%
S 613
12.1%
G 430
8.5%
L 421
8.3%
M 394
 
7.8%
P 354
 
7.0%
A 344
 
6.8%
R 340
 
6.7%
J 255
 
5.1%
B 187
 
3.7%
Other values (15) 996
19.7%
Decimal Number
ValueCountFrequency (%)
2 32
34.0%
9 30
31.9%
5 28
29.8%
1 4
 
4.3%
Space Separator
ValueCountFrequency (%)
1998
100.0%
Other Punctuation
ValueCountFrequency (%)
. 73
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29603
93.2%
Common 2165
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4195
14.2%
e 2638
 
8.9%
n 2373
 
8.0%
r 2090
 
7.1%
o 2084
 
7.0%
l 1814
 
6.1%
i 1453
 
4.9%
s 1020
 
3.4%
t 997
 
3.4%
u 986
 
3.3%
Other values (49) 9953
33.6%
Common
ValueCountFrequency (%)
1998
92.3%
. 73
 
3.4%
2 32
 
1.5%
9 30
 
1.4%
5 28
 
1.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30691
96.6%
None 1077
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4195
13.7%
e 2638
 
8.6%
n 2373
 
7.7%
r 2090
 
6.8%
o 2084
 
6.8%
1998
 
6.5%
l 1814
 
5.9%
i 1453
 
4.7%
s 1020
 
3.3%
t 997
 
3.2%
Other values (45) 10029
32.7%
None
ValueCountFrequency (%)
í 327
30.4%
ó 271
25.2%
á 226
21.0%
é 90
 
8.4%
ú 79
 
7.3%
ñ 50
 
4.6%
ü 22
 
2.0%
Ñ 8
 
0.7%
º 2
 
0.2%
Í 2
 
0.2%
Distinct2850
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:54.441909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length68
Median length43
Mean length12.31063
Min length4

Characters and Unicode

Total characters38680
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2689 ?
Unique (%)85.6%

Sample

1st row25 de Mayo
2nd rowDel Valle
3rd rowGobernador Ugarte
4th rowNorberto de la Riestra
5th rowLucas Monteverde
ValueCountFrequency (%)
villa 247
 
3.9%
san 226
 
3.6%
la 189
 
3.0%
de 189
 
3.0%
el 154
 
2.4%
los 103
 
1.6%
del 98
 
1.6%
est 85
 
1.3%
las 85
 
1.3%
general 73
 
1.2%
Other values (2609) 4850
77.0%
2023-07-07T10:58:55.714007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5093
 
13.2%
3201
 
8.3%
o 2735
 
7.1%
e 2676
 
6.9%
l 2466
 
6.4%
r 2422
 
6.3%
n 2203
 
5.7%
i 2099
 
5.4%
s 1515
 
3.9%
t 1163
 
3.0%
Other values (72) 13107
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28716
74.2%
Uppercase Letter 6315
 
16.3%
Space Separator 3201
 
8.3%
Other Punctuation 140
 
0.4%
Close Punctuation 102
 
0.3%
Open Punctuation 102
 
0.3%
Decimal Number 71
 
0.2%
Dash Punctuation 30
 
0.1%
Other Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5093
17.7%
o 2735
9.5%
e 2676
9.3%
l 2466
8.6%
r 2422
8.4%
n 2203
7.7%
i 2099
7.3%
s 1515
 
5.3%
t 1163
 
4.1%
u 1100
 
3.8%
Other values (24) 5244
18.3%
Uppercase Letter
ValueCountFrequency (%)
C 726
11.5%
S 612
 
9.7%
L 590
 
9.3%
P 455
 
7.2%
A 432
 
6.8%
M 418
 
6.6%
V 404
 
6.4%
E 379
 
6.0%
B 329
 
5.2%
R 319
 
5.1%
Other values (21) 1651
26.1%
Decimal Number
ValueCountFrequency (%)
1 19
26.8%
2 15
21.1%
3 8
11.3%
0 6
 
8.5%
5 6
 
8.5%
8 5
 
7.0%
9 5
 
7.0%
6 3
 
4.2%
4 3
 
4.2%
7 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 135
96.4%
' 5
 
3.6%
Space Separator
ValueCountFrequency (%)
3201
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35034
90.6%
Common 3646
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5093
14.5%
o 2735
 
7.8%
e 2676
 
7.6%
l 2466
 
7.0%
r 2422
 
6.9%
n 2203
 
6.3%
i 2099
 
6.0%
s 1515
 
4.3%
t 1163
 
3.3%
u 1100
 
3.1%
Other values (56) 11562
33.0%
Common
ValueCountFrequency (%)
3201
87.8%
. 135
 
3.7%
) 102
 
2.8%
( 102
 
2.8%
- 30
 
0.8%
1 19
 
0.5%
2 15
 
0.4%
3 8
 
0.2%
0 6
 
0.2%
5 6
 
0.2%
Other values (6) 22
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37870
97.9%
None 810
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5093
13.4%
3201
 
8.5%
o 2735
 
7.2%
e 2676
 
7.1%
l 2466
 
6.5%
r 2422
 
6.4%
n 2203
 
5.8%
i 2099
 
5.5%
s 1515
 
4.0%
t 1163
 
3.1%
Other values (58) 12297
32.5%
None
ValueCountFrequency (%)
í 223
27.5%
ó 166
20.5%
á 159
19.6%
é 125
15.4%
ñ 77
 
9.5%
ú 40
 
4.9%
ü 7
 
0.9%
Á 4
 
0.5%
º 3
 
0.4%
Ñ 2
 
0.2%
Other values (4) 4
 
0.5%
Distinct2718
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:56.343985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.8007638
Min length7

Characters and Unicode

Total characters24510
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2619 ?
Unique (%)83.4%

Sample

1st row6854100
2nd row6854020
3rd row6854040
4th row6854060
5th row6854050
ValueCountFrequency (%)
sin 72
 
2.2%
datos 72
 
2.2%
6441030 27
 
0.8%
6371010 27
 
0.8%
6840010 15
 
0.5%
6427010 15
 
0.5%
6638040 14
 
0.4%
6028010 12
 
0.4%
6805010 11
 
0.3%
6274010 11
 
0.3%
Other values (2709) 2938
91.4%
2023-07-07T10:58:57.205183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8458
34.5%
1 3118
 
12.7%
2 2163
 
8.8%
6 2135
 
8.7%
4 2060
 
8.4%
8 1488
 
6.1%
3 1367
 
5.6%
5 1234
 
5.0%
7 1079
 
4.4%
9 760
 
3.1%
Other values (9) 648
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23862
97.4%
Lowercase Letter 432
 
1.8%
Uppercase Letter 144
 
0.6%
Space Separator 72
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8458
35.4%
1 3118
 
13.1%
2 2163
 
9.1%
6 2135
 
8.9%
4 2060
 
8.6%
8 1488
 
6.2%
3 1367
 
5.7%
5 1234
 
5.2%
7 1079
 
4.5%
9 760
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
i 72
16.7%
s 72
16.7%
o 72
16.7%
t 72
16.7%
a 72
16.7%
n 72
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 72
50.0%
S 72
50.0%
Space Separator
ValueCountFrequency (%)
72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23934
97.6%
Latin 576
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8458
35.3%
1 3118
 
13.0%
2 2163
 
9.0%
6 2135
 
8.9%
4 2060
 
8.6%
8 1488
 
6.2%
3 1367
 
5.7%
5 1234
 
5.2%
7 1079
 
4.5%
9 760
 
3.2%
Latin
ValueCountFrequency (%)
i 72
12.5%
s 72
12.5%
o 72
12.5%
t 72
12.5%
a 72
12.5%
D 72
12.5%
n 72
12.5%
S 72
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8458
34.5%
1 3118
 
12.7%
2 2163
 
8.8%
6 2135
 
8.7%
4 2060
 
8.4%
8 1488
 
6.1%
3 1367
 
5.6%
5 1234
 
5.0%
7 1079
 
4.4%
9 760
 
3.1%
Other values (9) 648
 
2.6%

ADSL
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct724
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.07288
Minimum0
Maximum140791
Zeros2016
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:57.567432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3113.75
95-th percentile1923.4
Maximum140791
Range140791
Interquartile range (IQR)113.75

Descriptive statistics

Standard deviation3247.313
Coefficient of variation (CV)7.3125676
Kurtosis1177.4835
Mean444.07288
Median Absolute Deviation (MAD)0
Skewness30.213035
Sum1395277
Variance10545042
MonotonicityNot monotonic
2023-07-07T10:58:57.819879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2016
64.2%
1 53
 
1.7%
3 13
 
0.4%
2 13
 
0.4%
4 9
 
0.3%
15 8
 
0.3%
31 8
 
0.3%
30 8
 
0.3%
229 6
 
0.2%
37 6
 
0.2%
Other values (714) 1002
31.9%
ValueCountFrequency (%)
0 2016
64.2%
1 53
 
1.7%
2 13
 
0.4%
3 13
 
0.4%
4 9
 
0.3%
5 5
 
0.2%
6 5
 
0.2%
7 6
 
0.2%
8 2
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
140791 1
< 0.1%
58016 1
< 0.1%
52350 1
< 0.1%
46485 1
< 0.1%
20606 1
< 0.1%
19602 1
< 0.1%
18509 1
< 0.1%
16318 1
< 0.1%
15924 1
< 0.1%
15285 1
< 0.1%

CABLEMODEM
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct693
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1919.7868
Minimum0
Maximum1240125
Zeros2295
Zeros (%)73.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:58.091790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile7206.2
Maximum1240125
Range1240125
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23688.339
Coefficient of variation (CV)12.339047
Kurtosis2382.7677
Mean1919.7868
Median Absolute Deviation (MAD)0
Skewness46.145153
Sum6031970
Variance5.6113743 × 108
MonotonicityNot monotonic
2023-07-07T10:58:58.462629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2295
73.0%
1 35
 
1.1%
3 9
 
0.3%
10 6
 
0.2%
65 5
 
0.2%
12 5
 
0.2%
31 5
 
0.2%
2 4
 
0.1%
16 4
 
0.1%
53 4
 
0.1%
Other values (683) 770
 
24.5%
ValueCountFrequency (%)
0 2295
73.0%
1 35
 
1.1%
2 4
 
0.1%
3 9
 
0.3%
4 3
 
0.1%
5 2
 
0.1%
6 4
 
0.1%
7 3
 
0.1%
9 4
 
0.1%
10 6
 
0.2%
ValueCountFrequency (%)
1240125 1
< 0.1%
222667 1
< 0.1%
190777 1
< 0.1%
131591 1
< 0.1%
121533 1
< 0.1%
103773 1
< 0.1%
80744 1
< 0.1%
80087 1
< 0.1%
77825 1
< 0.1%
76855 1
< 0.1%

DIAL UP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct86
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9583068
Minimum0
Maximum2145
Zeros2760
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:58.747608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum2145
Range2145
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.704276
Coefficient of variation (CV)11.293788
Kurtosis1693.0958
Mean3.9583068
Median Absolute Deviation (MAD)0
Skewness36.895726
Sum12437
Variance1998.4723
MonotonicityNot monotonic
2023-07-07T10:58:59.048045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2760
87.8%
1 102
 
3.2%
2 48
 
1.5%
3 28
 
0.9%
4 23
 
0.7%
5 16
 
0.5%
8 11
 
0.4%
6 9
 
0.3%
7 7
 
0.2%
10 6
 
0.2%
Other values (76) 132
 
4.2%
ValueCountFrequency (%)
0 2760
87.8%
1 102
 
3.2%
2 48
 
1.5%
3 28
 
0.9%
4 23
 
0.7%
5 16
 
0.5%
6 9
 
0.3%
7 7
 
0.2%
8 11
 
0.4%
9 3
 
0.1%
ValueCountFrequency (%)
2145 1
< 0.1%
537 1
< 0.1%
533 1
< 0.1%
402 1
< 0.1%
343 1
< 0.1%
287 1
< 0.1%
248 1
< 0.1%
227 1
< 0.1%
215 1
< 0.1%
212 1
< 0.1%

FIBRA OPTICA
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct706
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean913.92139
Minimum0
Maximum208950
Zeros1743
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:59.294969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q357.75
95-th percentile3711.8
Maximum208950
Range208950
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation6213.1949
Coefficient of variation (CV)6.798391
Kurtosis550.53873
Mean913.92139
Median Absolute Deviation (MAD)0
Skewness20.542151
Sum2871541
Variance38603791
MonotonicityNot monotonic
2023-07-07T10:58:59.549363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1743
55.5%
1 177
 
5.6%
2 86
 
2.7%
5 45
 
1.4%
3 42
 
1.3%
4 29
 
0.9%
7 15
 
0.5%
6 15
 
0.5%
10 13
 
0.4%
8 11
 
0.4%
Other values (696) 966
30.7%
ValueCountFrequency (%)
0 1743
55.5%
1 177
 
5.6%
2 86
 
2.7%
3 42
 
1.3%
4 29
 
0.9%
5 45
 
1.4%
6 15
 
0.5%
7 15
 
0.5%
8 11
 
0.4%
9 8
 
0.3%
ValueCountFrequency (%)
208950 1
< 0.1%
129218 1
< 0.1%
127401 1
< 0.1%
103846 1
< 0.1%
87944 1
< 0.1%
39467 1
< 0.1%
39411 1
< 0.1%
38963 1
< 0.1%
36774 1
< 0.1%
33453 1
< 0.1%

OTROS
Real number (ℝ)

SKEWED  ZEROS 

Distinct219
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.207193
Minimum0
Maximum29585
Zeros2537
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:58:59.750084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile120.95
Maximum29585
Range29585
Interquartile range (IQR)0

Descriptive statistics

Standard deviation881.65907
Coefficient of variation (CV)12.739414
Kurtosis819.84739
Mean69.207193
Median Absolute Deviation (MAD)0
Skewness26.772016
Sum217449
Variance777322.71
MonotonicityNot monotonic
2023-07-07T10:58:59.985126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2537
80.7%
1 81
 
2.6%
2 33
 
1.1%
3 20
 
0.6%
6 18
 
0.6%
5 17
 
0.5%
10 15
 
0.5%
20 13
 
0.4%
15 12
 
0.4%
7 11
 
0.4%
Other values (209) 385
 
12.3%
ValueCountFrequency (%)
0 2537
80.7%
1 81
 
2.6%
2 33
 
1.1%
3 20
 
0.6%
4 11
 
0.4%
5 17
 
0.5%
6 18
 
0.6%
7 11
 
0.4%
8 5
 
0.2%
9 8
 
0.3%
ValueCountFrequency (%)
29585 1
< 0.1%
28954 1
< 0.1%
15215 1
< 0.1%
14585 1
< 0.1%
7199 1
< 0.1%
6084 1
< 0.1%
5283 1
< 0.1%
4987 1
< 0.1%
4797 1
< 0.1%
4170 1
< 0.1%

SATELITAL
Real number (ℝ)

SKEWED  ZEROS 

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0302355
Minimum0
Maximum975
Zeros2089
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:59:00.236233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum975
Range975
Interquartile range (IQR)1

Descriptive statistics

Standard deviation17.535193
Coefficient of variation (CV)17.020568
Kurtosis3033.065
Mean1.0302355
Median Absolute Deviation (MAD)0
Skewness54.618549
Sum3237
Variance307.48301
MonotonicityNot monotonic
2023-07-07T10:59:00.425099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2089
66.5%
1 653
 
20.8%
2 208
 
6.6%
3 69
 
2.2%
5 37
 
1.2%
4 33
 
1.1%
6 14
 
0.4%
7 10
 
0.3%
8 4
 
0.1%
11 3
 
0.1%
Other values (14) 22
 
0.7%
ValueCountFrequency (%)
0 2089
66.5%
1 653
 
20.8%
2 208
 
6.6%
3 69
 
2.2%
4 33
 
1.1%
5 37
 
1.2%
6 14
 
0.4%
7 10
 
0.3%
8 4
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
975 1
 
< 0.1%
57 1
 
< 0.1%
50 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
18 1
 
< 0.1%
16 3
0.1%

WIMAX
Real number (ℝ)

SKEWED  ZEROS 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67059198
Minimum0
Maximum444
Zeros3131
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:59:00.660787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum444
Range444
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.883203
Coefficient of variation (CV)22.194126
Kurtosis681.2448
Mean0.67059198
Median Absolute Deviation (MAD)0
Skewness25.483409
Sum2107
Variance221.50973
MonotonicityNot monotonic
2023-07-07T10:59:01.037251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3131
99.6%
444 1
 
< 0.1%
325 1
 
< 0.1%
16 1
 
< 0.1%
437 1
 
< 0.1%
90 1
 
< 0.1%
360 1
 
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
224 1
 
< 0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 3131
99.6%
2 1
 
< 0.1%
16 1
 
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
90 1
 
< 0.1%
92 1
 
< 0.1%
224 1
 
< 0.1%
325 1
 
< 0.1%
360 1
 
< 0.1%
ValueCountFrequency (%)
444 1
< 0.1%
437 1
< 0.1%
360 1
< 0.1%
325 1
< 0.1%
224 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
16 1
< 0.1%

WIRELESS
Real number (ℝ)

ZEROS 

Distinct606
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.31063
Minimum0
Maximum18447
Zeros934
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:59:01.241278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q3113
95-th percentile795.55
Maximum18447
Range18447
Interquartile range (IQR)113

Descriptive statistics

Standard deviation685.35234
Coefficient of variation (CV)3.8652637
Kurtosis308.96256
Mean177.31063
Median Absolute Deviation (MAD)21
Skewness14.633034
Sum557110
Variance469707.83
MonotonicityNot monotonic
2023-07-07T10:59:01.445277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 934
29.7%
1 104
 
3.3%
2 74
 
2.4%
5 67
 
2.1%
3 62
 
2.0%
4 31
 
1.0%
10 28
 
0.9%
20 27
 
0.9%
6 26
 
0.8%
8 23
 
0.7%
Other values (596) 1766
56.2%
ValueCountFrequency (%)
0 934
29.7%
1 104
 
3.3%
2 74
 
2.4%
3 62
 
2.0%
4 31
 
1.0%
5 67
 
2.1%
6 26
 
0.8%
7 23
 
0.7%
8 23
 
0.7%
9 21
 
0.7%
ValueCountFrequency (%)
18447 1
< 0.1%
16749 1
< 0.1%
10179 1
< 0.1%
8310 1
< 0.1%
8182 1
< 0.1%
7692 1
< 0.1%
6781 1
< 0.1%
5758 1
< 0.1%
5713 1
< 0.1%
5228 1
< 0.1%

Total general
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1348
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3529.958
Minimum0
Maximum1547679
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-07-07T10:59:01.696585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q122.25
median151
Q3982.25
95-th percentile13315.65
Maximum1547679
Range1547679
Interquartile range (IQR)960

Descriptive statistics

Standard deviation31554.951
Coefficient of variation (CV)8.9391859
Kurtosis1851.02
Mean3529.958
Median Absolute Deviation (MAD)148
Skewness39.340908
Sum11091128
Variance9.9571492 × 108
MonotonicityNot monotonic
2023-07-07T10:59:01.900598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 193
 
6.1%
2 102
 
3.2%
3 62
 
2.0%
5 59
 
1.9%
4 44
 
1.4%
6 35
 
1.1%
7 34
 
1.1%
8 30
 
1.0%
10 27
 
0.9%
20 24
 
0.8%
Other values (1338) 2532
80.6%
ValueCountFrequency (%)
0 5
 
0.2%
1 193
6.1%
2 102
3.2%
3 62
 
2.0%
4 44
 
1.4%
5 59
 
1.9%
6 35
 
1.1%
7 34
 
1.1%
8 30
 
1.0%
9 23
 
0.7%
ValueCountFrequency (%)
1547679 1
< 0.1%
495182 1
< 0.1%
330727 1
< 0.1%
242888 1
< 0.1%
198179 1
< 0.1%
187467 1
< 0.1%
135364 1
< 0.1%
127356 1
< 0.1%
121243 1
< 0.1%
112298 1
< 0.1%

Interactions

2023-07-07T10:58:48.555514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:31.596469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:33.856017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:35.887076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:38.029713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:40.015639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:42.116663image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:44.283180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:46.238196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:48.769467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:31.836504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:34.031163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:36.094948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:38.277618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:40.238690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:42.335951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:44.532823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:46.501383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:48.965854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:32.030679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:34.263847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:36.437470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:38.479578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:40.486941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:42.557779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:44.718859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:46.705832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:49.178220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:32.261614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:34.511289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:36.690832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:38.653515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:40.667835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:42.772863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:44.937841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:46.971995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:49.402739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:32.519524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:34.728485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:36.884298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:38.876555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:40.908844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:43.057444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:45.160921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:47.174247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:49.660818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:32.715375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:34.956238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:37.073389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:39.097141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:41.160643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:43.332371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:45.393296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:47.434484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:49.860067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:32.961408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:35.174803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:37.285843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:39.319149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:41.399921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:43.560514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:45.585282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:47.657146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:50.047800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:33.190899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:35.413105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:37.528611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:39.526692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:41.625648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:43.750397image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:45.779521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:48.026087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:50.239568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:33.413325image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:35.681654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:37.790183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:39.765275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:41.827354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:44.013841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:46.012680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-07T10:58:48.289999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-07T10:59:02.088958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ADSLCABLEMODEMDIAL UPFIBRA OPTICAOTROSSATELITALWIMAXWIRELESSTotal generalProvincia
ADSL1.0000.4640.4040.4430.3310.1180.0700.1930.6740.497
CABLEMODEM0.4641.0000.4070.4450.3420.1010.0620.1140.6300.703
DIAL UP0.4040.4071.0000.3780.3660.1580.0770.2060.4300.576
FIBRA OPTICA0.4430.4450.3781.0000.3090.1110.0460.1120.6050.350
OTROS0.3310.3420.3660.3091.0000.1180.0150.0870.3970.314
SATELITAL0.1180.1010.1580.1110.1181.0000.0250.0360.0870.000
WIMAX0.0700.0620.0770.0460.0150.0251.0000.0090.0650.000
WIRELESS0.1930.1140.2060.1120.0870.0360.0091.0000.4630.242
Total general0.6740.6300.4300.6050.3970.0870.0650.4631.0000.496
Provincia0.4970.7030.5760.3500.3140.0000.0000.2420.4961.000

Missing values

2023-07-07T10:58:50.554724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-07T10:58:51.006684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ProvinciaPartidoLocalidadLink IndecADSLCABLEMODEMDIAL UPFIBRA OPTICAOTROSSATELITALWIMAXWIRELESSTotal general
0BUENOS AIRES25 de Mayo25 de Mayo6854100873470402097506647218
1BUENOS AIRES25 de MayoDel Valle685402018100010100192
2BUENOS AIRES25 de MayoGobernador Ugarte68540400000000181181
3BUENOS AIRES25 de MayoNorberto de la Riestra6854060078206167003271282
4BUENOS AIRES25 de MayoLucas Monteverde6854050000000066
5BUENOS AIRES25 de MayoErnestina685403066000000066
6BUENOS AIRES25 de MayoPedernales68540705680000007575
7BUENOS AIRES9 de Julio12 de Octubre658803000000001919
8BUENOS AIRES9 de Julio9 de Julio658810043603362825886831083114605
9BUENOS AIRES9 de JulioAlfredo Demarchi (Est. Facundo Quiroga)658801045000060000510
ProvinciaPartidoLocalidadLink IndecADSLCABLEMODEMDIAL UPFIBRA OPTICAOTROSSATELITALWIMAXWIRELESSTotal general
3132TUCUMANTafí del ValleTafí del Valle9009804016800011035205
3133TUCUMANTafí ViejoBarrio Lomas de Tafí901050203490000000349
3134TUCUMANTafí ViejoBarrio Mutual San Martín9010503023000000023
3135TUCUMANTafí ViejoTafí Viejo901050805620622069011100613160
3136TUCUMANTafí ViejoDiagonal Norte - Luz y Fuerza - Los Pocitos - Villa Nueva Italia (6)901050600002810000281
3137TUCUMANTafí ViejoVilla Mariano Moreno - El Colmenar901051000104710000472
3138TUCUMANTrancasSan Pedro de Colalao901120200000000313313
3139TUCUMANTrancasVilla de Trancas90112030150000330099282
3140TUCUMANYerba BuenaVilla Carmela901190201725000100971823
3141TUCUMANYerba BuenaYerba Buena - Marcos Paz901190301292030155113300015876